FedAlign通过跨客户端特征对齐，提升联邦学习下的域泛化能力。
FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment
- 跨客户端特征扩展增强局部数据多样性。
- 双阶段对齐优化全局特征，提升域不变性。
- 轻量设计适合隐私敏感场景，计算开销小。
联邦学习（FL）提供了无需直接共享数据的去中心化协作训练范式，但在域泛化（DG）方面面临严格隐私约束、本地数据非独立同分布（non-i.i.d.）以及域多样性有限等挑战。本文提出FedAlign，一种轻量级、隐私保护的框架，通过同时提升特征多样性与促进域不变性来增强联邦环境中的域泛化能力。首先，跨客户端特征扩展模块通过域不变特征扰动和选择性跨客户端特征传输，拓宽各客户端的局部域表征，使其安全地访问更丰富的域空间。其次，双阶段对齐模块通过在客户端间对齐特征嵌入与预测结果，优化全局特征学习，提炼出鲁棒的域不变特征。集成该模块后，方法在未见域上实现优异泛化性能，同时保持数据隐私，并具备极低的计算与通信开销。
原文摘要 · Abstract (English)
Federated Learning (FL) offers a decentralized paradigm for collaborative model training without direct data sharing, yet it poses unique challenges for Domain Generalization (DG), including strict privacy constraints, non-i.i.d. local data, and limited domain diversity. We introduce FedAlign, a lightweight, privacy-preserving framework designed to enhance DG in federated settings by simultaneously increasing feature diversity and promoting domain invariance. First, a cross-client feature extension module broadens local domain representations through domain-invariant feature perturbation and selective cross-client feature transfer, allowing each client to safely access a richer domain space. Second, a dual-stage alignment module refines global feature learning by aligning both feature embeddings and predictions across clients, thereby distilling robust, domain-invariant features. By integrating these modules, our method achieves superior generalization to unseen domains while maintaining data privacy and operating with minimal computational and communication overhead.
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